Speeding Up Problem-Solving by Abstraction: A Graph
... abstraction is guaranteed to speed up search in a wide range of commonly occurring circumstances. Several new refinement techniques are presented, including one (AltO) that is superior in terms of robustness and performance. Although developed by analyzing existing refinement techniques from the gra ...
... abstraction is guaranteed to speed up search in a wide range of commonly occurring circumstances. Several new refinement techniques are presented, including one (AltO) that is superior in terms of robustness and performance. Although developed by analyzing existing refinement techniques from the gra ...
45 Online Planning for Large Markov Decision Processes
... single timestep instead of the entire state space. This makes them a preferable choice in many real-world applications, including RoboCup 2D. However, the agent must come up with a plan for the current state in almost real time because computation time is usually very limited for online decision mak ...
... single timestep instead of the entire state space. This makes them a preferable choice in many real-world applications, including RoboCup 2D. However, the agent must come up with a plan for the current state in almost real time because computation time is usually very limited for online decision mak ...
Solvability of Some Nonlinear Fourth Order Boundary Value Problems
... This thesis is concerned with the study of certain classes of nonlinear fourth order boundary value problems. They are motivated by some physical problems. Sufficient conditions for the existence of solutions under various assumptions are presented. After an introductory chapter, we discuss (in Chap ...
... This thesis is concerned with the study of certain classes of nonlinear fourth order boundary value problems. They are motivated by some physical problems. Sufficient conditions for the existence of solutions under various assumptions are presented. After an introductory chapter, we discuss (in Chap ...
Artificial Intelligence UNIT I Page 1 of 116 CSE– Dhaanish Ahmed
... “It is not my aim to surprise or shock you-but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until-in a visible future-the range of problems they can ...
... “It is not my aim to surprise or shock you-but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until-in a visible future-the range of problems they can ...
Incremental Heuristic Search in Artificial Intelligence
... We now discuss one particular way of solving fully dynamic shortest path problems. As an example, we use route-planning in known eight-connected gridworlds with cells whose traversability changes over time. They are either traversable (with cost one) or untraversable. The routeplanning problem is to ...
... We now discuss one particular way of solving fully dynamic shortest path problems. As an example, we use route-planning in known eight-connected gridworlds with cells whose traversability changes over time. They are either traversable (with cost one) or untraversable. The routeplanning problem is to ...
Incremental Heuristic Search in AI
... We now discuss one particular way of solving fully dynamic shortest-path problems. As an example, we use route planning in known eight-connected gridworlds with cells whose traversability changes over time. They are either traversable (with cost one) or untraversable. The route-planning problem is t ...
... We now discuss one particular way of solving fully dynamic shortest-path problems. As an example, we use route planning in known eight-connected gridworlds with cells whose traversability changes over time. They are either traversable (with cost one) or untraversable. The route-planning problem is t ...
Iterative implementation of depth first
... • From AI admirers to AI programmers. • Step 1: Represent the problem so that it is computerfriendly. • Step 2: Code the problem in a programming language. • Step 3: Develop/code an algorithm to find a solution. • Step 4: Represent the solution so that it is humanfriendly. ...
... • From AI admirers to AI programmers. • Step 1: Represent the problem so that it is computerfriendly. • Step 2: Code the problem in a programming language. • Step 3: Develop/code an algorithm to find a solution. • Step 4: Represent the solution so that it is humanfriendly. ...
Monte-Carlo Tree Search for the Multiple Sequence Alignment Problem
... expansion (Hatem and Ruml 2013). Still, the memory requirements raise exponentially with the problem complexity (measured in the sum of the input sequences). In this paper we apply fixed-memory-bound randomized search that incorporates no expert knowledge in form of refined heuristics. The algorithm ...
... expansion (Hatem and Ruml 2013). Still, the memory requirements raise exponentially with the problem complexity (measured in the sum of the input sequences). In this paper we apply fixed-memory-bound randomized search that incorporates no expert knowledge in form of refined heuristics. The algorithm ...
A comprehensive survey of multi
... feedback is less informative than in supervised learning, where the agent would be given the correct actions to take [9] (such information is unfortunately not always available). The RL feedback is, however, more informative than in unsupervised learning, where the agent would be left to discover th ...
... feedback is less informative than in supervised learning, where the agent would be given the correct actions to take [9] (such information is unfortunately not always available). The RL feedback is, however, more informative than in unsupervised learning, where the agent would be left to discover th ...
md hassan - Computer and Information Science
... Stanford developed models they called ADALINE and MADALINE. These models were named for their use of Multiple ADAptive LINear Elements. MADALINE was the first neural network to be applied to a real world problem [4]. ...
... Stanford developed models they called ADALINE and MADALINE. These models were named for their use of Multiple ADAptive LINear Elements. MADALINE was the first neural network to be applied to a real world problem [4]. ...
Environments and Problem Solving Methods
... 4. Introduction: Environments and Problem Solving Methods ...
... 4. Introduction: Environments and Problem Solving Methods ...
Foundations of Artificial Intelligence
... 4. Introduction: Environments and Problem Solving Methods ...
... 4. Introduction: Environments and Problem Solving Methods ...
Consensus group stable feature selection
... (10 ± 5) highly correlated features to each of these 100 features. Within each correlated group, the Pearson correlation of each feature pair is within (0.5,1), and the average pairwise correlation is below 0.75. The balanced binary class label Y is decided based on X1 , X2 , ..., X10 using a linear ...
... (10 ± 5) highly correlated features to each of these 100 features. Within each correlated group, the Pearson correlation of each feature pair is within (0.5,1), and the average pairwise correlation is below 0.75. The balanced binary class label Y is decided based on X1 , X2 , ..., X10 using a linear ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.